{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f776d1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORT Images from directory. Use this if using a generic processing function. If model-specific go to next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03eb8f73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70de08e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use this if using model-specific pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1894660",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, optimizers\n",
    "#from tensorflow.keras.applications import VGG16\n",
    "#from tensorflow.keras.applications import resnet50\n",
    "from tensorflow.keras.applications import densenet\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import os\n",
    "from glob import glob\n",
    "#from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "from tensorflow.keras.applications.densenet import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "# Set your directory containing the images\n",
    "data_directory = 'C:\\\\Users\\\\oscar\\\\Documents\\\\MOMOCS\\\\FOUR_CARNIVORES'\n",
    "\n",
    "# Function to load and resize images from a directory\n",
    "\n",
    "def load_images(directory, target_size=(250, 200)):\n",
    "    image_list = []\n",
    "    file_pattern = os.path.join(directory, '*.bmp')  # Modify as needed for other file types\n",
    "    for filename in glob(file_pattern):\n",
    "        print(f\"Loading, resizing, and preprocessing image: {filename}\")\n",
    "        img = load_img(filename, target_size=target_size)\n",
    "        img_array = img_to_array(img)\n",
    "        img_array = preprocess_input(img_array)  # Apply VGG16 preprocessing\n",
    "        image_list.append(img_array)\n",
    "    return np.array(image_list)\n",
    "\n",
    "\n",
    "# Load images for each class\n",
    "class1_images = load_images(os.path.join(data_directory, 'crocs'))\n",
    "class2_images = load_images(os.path.join(data_directory, 'hyenas'))\n",
    "class3_images = load_images(os.path.join(data_directory, 'leopards'))\n",
    "class4_images = load_images(os.path.join(data_directory, 'lions'))\n",
    "\n",
    "# Create labels for each class\n",
    "class1_labels = np.zeros(len(class1_images))\n",
    "class2_labels = np.ones(len(class2_images))\n",
    "class3_labels = 2 * np.ones(len(class3_images))\n",
    "class4_labels = 3 * np.ones(len(class4_images))\n",
    "\n",
    "# Concatenate images and labels for all classes\n",
    "all_images = np.concatenate([class1_images, class2_images, class3_images, class4_images])\n",
    "all_labels = np.concatenate([class1_labels, class2_labels, class3_labels, class4_labels])\n",
    "\n",
    "\n",
    "# Split the data into training, validation, and testing sets\n",
    "train_images, test_images, train_labels, test_labels = train_test_split(\n",
    "    all_images, all_labels, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "train_images, val_images, train_labels, val_labels = train_test_split(\n",
    "    train_images, train_labels, test_size=0.2, random_state=42\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96a1bc38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training images: 351\n",
      "Number of validation images: 88\n",
      "Number of testing images: 110\n"
     ]
    }
   ],
   "source": [
    "num_training_images = len(train_images)\n",
    "print(f'Number of training images: {num_training_images}')\n",
    "\n",
    "num_validation_images = len(val_images)\n",
    "print(f'Number of validation images: {num_validation_images}')\n",
    "\n",
    "num_testing_images = len(test_images)\n",
    "print(f'Number of testing images: {num_testing_images}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f4b02bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(88, 250, 200, 3)\n"
     ]
    }
   ],
   "source": [
    "print(class1_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfae7a6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc0d0296",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "814db61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOW APPLY MODELS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e194a99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a531d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with regularization: Dropout, Early Stopping, Regulatrized Learning rate (Learning Rate Scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6388cdd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5e0f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USE THIS ONE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "777c33d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#play with number of tasks and shots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a566924",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with regularization: Dropout, Early Stopping, Regulatrized Learning rate (Learning Rate Scheduler) and Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d9c62ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained VGG16 model without the top classification layers\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(250, 200, 3))\n",
    "\n",
    "# Freeze the convolutional layers of the VGG16 base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00a4fa22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import DenseNet201\n",
    "\n",
    "# Load the pre-trained DenseNet201 model without the top classification layers\n",
    "base_model = DenseNet201(weights='imagenet', include_top=False, input_shape=(250, 200, 3))\n",
    "\n",
    "# Freeze the convolutional layers of the DenseNet201 base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e562476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained ResNet50 model without the top classification layers\n",
    "base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_shape=(250, 200, 3))\n",
    "\n",
    "# Freeze the convolutional layers of the VGG16 base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4d8eaa5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 - Train Loss: 0.6884, Train Accuracy: 69.23%\n",
      "Epoch 1/100 - Validation Loss: 0.7089, Validation Accuracy: 72.73%\n",
      "Learning Rate: 0.001\n",
      "Epoch 2/100 - Train Loss: 0.6348, Train Accuracy: 75.50%\n",
      "Epoch 2/100 - Validation Loss: 0.6462, Validation Accuracy: 77.27%\n",
      "Learning Rate: 0.001\n",
      "Epoch 3/100 - Train Loss: 0.4723, Train Accuracy: 78.92%\n",
      "Epoch 3/100 - Validation Loss: 0.5794, Validation Accuracy: 76.14%\n",
      "Learning Rate: 0.001\n",
      "Epoch 4/100 - Train Loss: 0.3887, Train Accuracy: 83.76%\n",
      "Epoch 4/100 - Validation Loss: 0.5890, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.001\n",
      "Epoch 5/100 - Train Loss: 0.3633, Train Accuracy: 84.62%\n",
      "Epoch 5/100 - Validation Loss: 0.7749, Validation Accuracy: 75.00%\n",
      "Learning Rate: 0.001\n",
      "Epoch 6/100 - Train Loss: 0.3373, Train Accuracy: 87.18%\n",
      "Epoch 6/100 - Validation Loss: 0.5849, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.001\n",
      "Epoch 7/100 - Train Loss: 0.3629, Train Accuracy: 85.75%\n",
      "Epoch 7/100 - Validation Loss: 0.7544, Validation Accuracy: 75.00%\n",
      "Learning Rate: 0.001\n",
      "Epoch 8/100 - Train Loss: 0.3329, Train Accuracy: 86.61%\n",
      "Epoch 8/100 - Validation Loss: 0.6891, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.001\n",
      "Epoch 9/100 - Train Loss: 0.2920, Train Accuracy: 86.61%\n",
      "Epoch 9/100 - Validation Loss: 0.7192, Validation Accuracy: 77.27%\n",
      "Learning Rate: 0.001\n",
      "Epoch 10/100 - Train Loss: 0.2555, Train Accuracy: 89.74%\n",
      "Epoch 10/100 - Validation Loss: 0.6413, Validation Accuracy: 76.14%\n",
      "Learning Rate: 0.001\n",
      "Epoch 11/100 - Train Loss: 0.4300, Train Accuracy: 84.90%\n",
      "Epoch 11/100 - Validation Loss: 1.2334, Validation Accuracy: 76.14%\n",
      "Learning Rate: 0.001\n",
      "Epoch 12/100 - Train Loss: 0.2258, Train Accuracy: 90.31%\n",
      "Epoch 12/100 - Validation Loss: 0.6644, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 13/100 - Train Loss: 0.1304, Train Accuracy: 94.59%\n",
      "Epoch 13/100 - Validation Loss: 0.6085, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 14/100 - Train Loss: 0.1194, Train Accuracy: 94.87%\n",
      "Epoch 14/100 - Validation Loss: 0.5930, Validation Accuracy: 77.27%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 15/100 - Train Loss: 0.1292, Train Accuracy: 95.44%\n",
      "Epoch 15/100 - Validation Loss: 0.5673, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 16/100 - Train Loss: 0.1074, Train Accuracy: 96.01%\n",
      "Epoch 16/100 - Validation Loss: 0.5240, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 17/100 - Train Loss: 0.1113, Train Accuracy: 96.01%\n",
      "Epoch 17/100 - Validation Loss: 0.5722, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 18/100 - Train Loss: 0.1074, Train Accuracy: 96.30%\n",
      "Epoch 18/100 - Validation Loss: 0.6025, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 19/100 - Train Loss: 0.1208, Train Accuracy: 96.01%\n",
      "Epoch 19/100 - Validation Loss: 0.6310, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 20/100 - Train Loss: 0.1144, Train Accuracy: 96.30%\n",
      "Epoch 20/100 - Validation Loss: 0.6206, Validation Accuracy: 78.41%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 21/100 - Train Loss: 0.1227, Train Accuracy: 96.01%\n",
      "Epoch 21/100 - Validation Loss: 0.6589, Validation Accuracy: 79.55%\n",
      "Learning Rate: 0.0001\n",
      "Epoch 22/100 - Train Loss: 0.1352, Train Accuracy: 94.59%\n",
      "Epoch 22/100 - Validation Loss: 0.6909, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 23/100 - Train Loss: 0.1265, Train Accuracy: 96.01%\n",
      "Epoch 23/100 - Validation Loss: 0.6613, Validation Accuracy: 79.55%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 24/100 - Train Loss: 0.1244, Train Accuracy: 96.01%\n",
      "Epoch 24/100 - Validation Loss: 0.6552, Validation Accuracy: 79.55%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 25/100 - Train Loss: 0.1228, Train Accuracy: 95.73%\n",
      "Epoch 25/100 - Validation Loss: 0.6565, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 26/100 - Train Loss: 0.1267, Train Accuracy: 95.73%\n",
      "Epoch 26/100 - Validation Loss: 0.6701, Validation Accuracy: 79.55%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 27/100 - Train Loss: 0.1269, Train Accuracy: 95.73%\n",
      "Epoch 27/100 - Validation Loss: 0.6707, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 28/100 - Train Loss: 0.1197, Train Accuracy: 96.58%\n",
      "Epoch 28/100 - Validation Loss: 0.6479, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 29/100 - Train Loss: 0.1170, Train Accuracy: 96.58%\n",
      "Epoch 29/100 - Validation Loss: 0.6379, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 30/100 - Train Loss: 0.1169, Train Accuracy: 96.58%\n",
      "Epoch 30/100 - Validation Loss: 0.6442, Validation Accuracy: 78.41%\n",
      "Learning Rate: 1e-05\n",
      "Epoch 31/100 - Train Loss: 0.1166, Train Accuracy: 96.58%\n",
      "Epoch 31/100 - Validation Loss: 0.6416, Validation Accuracy: 78.41%\n",
      "Early stopping at epoch 31\n",
      "Final Testing Loss: 1.0121, Testing Accuracy: 75.45%\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "# Set random seed for TensorFlow\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Set random seed for numpy\n",
    "np.random.seed(42)\n",
    "\n",
    "# Set random seed for Python random module\n",
    "random.seed(42)\n",
    "\n",
    "# Now your entire script will produce reproducible results as long as it doesn't rely on non-deterministic operations\n",
    "\n",
    "# Create MAML model\n",
    "def create_maml_model(base_model, num_classes):\n",
    "    maml_model = models.Sequential([\n",
    "        base_model,\n",
    "        layers.Conv2D(512, (3, 3), activation='relu'),\n",
    "        layers.GlobalAveragePooling2D(),  # Replace max-pooling with GlobalAveragePooling\n",
    "        layers.Dense(512, activation='relu'),\n",
    "        layers.Dropout(0.6),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.Dense(num_classes, activation='softmax')\n",
    "    ])\n",
    "    return maml_model\n",
    "\n",
    "# Set up MAML model\n",
    "num_classes = 4  # Number of output classes\n",
    "maml_model = create_maml_model(base_model, num_classes)\n",
    "\n",
    "# Set up MAML optimizer\n",
    "meta_optimizer = optimizers.Adam(learning_rate=1e-3)\n",
    "\n",
    "# Compile the MAML model\n",
    "maml_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "# Lists to store training, validation, and testing history\n",
    "train_loss_history = []\n",
    "train_acc_history = []\n",
    "val_loss_history = []\n",
    "val_acc_history = []\n",
    "test_loss_history = []\n",
    "test_acc_history = []\n",
    "\n",
    "# Training loop for few-shot learning\n",
    "num_epochs = 100  # Increase the number of epochs\n",
    "num_tasks = 20  # Number of few-shot tasks\n",
    "validation_interval = 1  # Evaluate on the validation set every 'validation_interval' epochs\n",
    "num_shots = 10  # Number of examples per class in each few-shot task\n",
    "\n",
    "# Early stopping parameters\n",
    "patience = 15\n",
    "best_val_loss = float('inf')\n",
    "wait = 0\n",
    "\n",
    "# Data Augmentation\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "# Callbacks\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=patience,\n",
    "    restore_best_weights=True\n",
    ")\n",
    "# Define the directory where you want to save the best model\n",
    "save_dir = 'C:\\\\Users\\\\oscar\\\\Documents\\\\MOMOCS\\\\FOUR_CARNIVORES'\n",
    "\n",
    "# Callback to save the best model\n",
    "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "    os.path.join(save_dir, 'best_model.h5'),  # Full path to save the best model\n",
    "    monitor='val_loss',\n",
    "    save_best_only=True\n",
    ")\n",
    "\n",
    "\n",
    "# Learning rate scheduler function\n",
    "def lr_schedule(epoch):\n",
    "    lr = 1e-3\n",
    "    if epoch > 10:\n",
    "        lr *= 0.1\n",
    "    if epoch > 20:\n",
    "        lr *= 0.1\n",
    "    if epoch > 30:\n",
    "        lr *= 0.1\n",
    "    return lr\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    for task in range(num_tasks):\n",
    "        task_samples = []\n",
    "        task_labels = []\n",
    "        for class_idx in range(num_classes):\n",
    "            class_indices = np.where(train_labels == class_idx)[0]\n",
    "            selected_indices = np.random.choice(class_indices, num_shots, replace=False)\n",
    "            selected_samples = train_images[selected_indices]\n",
    "            task_samples.extend(selected_samples)\n",
    "            task_labels.extend([class_idx] * num_shots)\n",
    "\n",
    "        task_samples = np.array(task_samples)\n",
    "        task_labels = np.array(task_labels)\n",
    "\n",
    "        # Fine-tune the model on the few-shot task with data augmentation\n",
    "        with tf.GradientTape() as tape:\n",
    "            augmented_task_samples = []\n",
    "            for sample in task_samples:\n",
    "                augmented_sample = datagen.random_transform(sample)\n",
    "                augmented_task_samples.append(augmented_sample)\n",
    "            augmented_task_samples = np.array(augmented_task_samples)\n",
    "\n",
    "            logits = maml_model(augmented_task_samples)\n",
    "            loss = tf.losses.sparse_categorical_crossentropy(task_labels, logits)\n",
    "\n",
    "        gradients = tape.gradient(loss, maml_model.trainable_variables)\n",
    "        meta_optimizer.apply_gradients(zip(gradients, maml_model.trainable_variables))\n",
    "\n",
    "    train_loss, train_acc = maml_model.evaluate(train_images, train_labels, verbose=0)\n",
    "    print(f'Epoch {epoch + 1}/{num_epochs} - Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc * 100:.2f}%')\n",
    "\n",
    "    train_loss_history.append(train_loss)\n",
    "    train_acc_history.append(train_acc)\n",
    "\n",
    "    if epoch % validation_interval == 0:\n",
    "        val_loss, val_acc = maml_model.evaluate(val_images, val_labels, verbose=0)\n",
    "        print(f'Epoch {epoch + 1}/{num_epochs} - Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_acc * 100:.2f}%')\n",
    "        \n",
    "        val_loss_history.append(val_loss)\n",
    "        val_acc_history.append(val_acc)\n",
    "\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            wait = 0\n",
    "            # Save the best model\n",
    "            maml_model.save(os.path.join(save_dir, 'best_model.h5'))\n",
    "        else:\n",
    "            wait += 1\n",
    "            if wait >= patience:\n",
    "                print(f'Early stopping at epoch {epoch + 1}')\n",
    "                break\n",
    "\n",
    "        # Apply learning rate scheduler\n",
    "    lr = lr_schedule(epoch)\n",
    "    tf.keras.backend.set_value(meta_optimizer.lr, lr)\n",
    "    print(f'Learning Rate: {lr}')\n",
    "\n",
    "# After training, you can use the test set for the final evaluation\n",
    "test_loss, test_acc = maml_model.evaluate(test_images, test_labels, verbose=0)\n",
    "print(f'Final Testing Loss: {test_loss:.4f}, Testing Accuracy: {test_acc * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d5a21728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plotting the training and validation history\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_loss_history, label='Train Loss')\n",
    "plt.plot(val_loss_history, label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_acc_history, label='Train Accuracy')\n",
    "plt.plot(val_acc_history, label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c85f6636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Testing Loss: 1.3849, Testing Accuracy: 75.45%\n",
      "4/4 [==============================] - 6s 1s/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.69      0.80        26\n",
      "         1.0       0.76      0.82      0.78        38\n",
      "         2.0       0.63      0.63      0.63        19\n",
      "         3.0       0.71      0.81      0.76        27\n",
      "\n",
      "    accuracy                           0.75       110\n",
      "   macro avg       0.76      0.74      0.74       110\n",
      "weighted avg       0.77      0.75      0.76       110\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# After training, you can use the test set for the final evaluation\n",
    "test_loss, test_acc = maml_model.evaluate(test_images, test_labels, verbose=0)\n",
    "print(f'Final Testing Loss: {test_loss:.4f}, Testing Accuracy: {test_acc * 100:.2f}%')\n",
    "\n",
    "# Predict probabilities for test images\n",
    "y_pred_prob = maml_model.predict(test_images)\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Generate classification report\n",
    "print(classification_report(test_labels, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "069942e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f058cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#classify new images:\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "\n",
    "# Assuming maml_model is the variable holding your trained model\n",
    "# maml_model = create_maml_model(base_model, num_classes)  # Replace with your model creation code\n",
    "\n",
    "# Path to the folder containing images\n",
    "folder_path = 'C:\\\\Users\\\\oscar\\\\Documents\\\\MOMOCS\\\\select'\n",
    "\n",
    "# List all files in the folder\n",
    "image_files = os.listdir(folder_path)\n",
    "\n",
    "# Iterate over each image file\n",
    "for file_name in image_files:\n",
    "    # Load the input image\n",
    "    img_path = os.path.join(folder_path, file_name)\n",
    "    img = image.load_img(img_path, target_size=(250, 200))\n",
    "    img_array = image.img_to_array(img)\n",
    "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
    "\n",
    "    # Preprocess the input image using ResNet50 preprocessing\n",
    "    img_array = preprocess_input(img_array)\n",
    "\n",
    "    # Use the model to make predictions\n",
    "    predictions = maml_model.predict(img_array)\n",
    "\n",
    "    # Get the predicted class index\n",
    "    predicted_class_index = np.argmax(predictions[0])\n",
    "\n",
    "    # Get the predicted class probability\n",
    "    predicted_class_probability = predictions[0][predicted_class_index]\n",
    "\n",
    "    # Get class labels (assuming you have them)\n",
    "    # Replace class_labels with your list of class labels\n",
    "    # class_labels = [...]\n",
    "\n",
    "    # Print the result\n",
    "    print(f'Image: {file_name}')\n",
    "    print(f'Predicted class index: {predicted_class_index}')\n",
    "    print(f'Predicted class probability: {predicted_class_probability}')\n",
    "    print('Probabilities for other classes:')\n",
    "    for i, prob in enumerate(predictions[0]):\n",
    "        print(f'Class {i}: {prob}')\n",
    "    print('---------------------------------------------')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d63d3ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if using a saved model:\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "import numpy as np\n",
    "\n",
    "# Step 1: Load the pre-trained model\n",
    "model_path = 'path/to/your/model.h5'\n",
    "loaded_model = load_model(model_path)\n",
    "\n",
    "# Step 2: Preprocess the new image\n",
    "img_path = 'path/to/your/new_image.jpg'\n",
    "img = image.load_img(img_path, target_size=(100, 100))  # Adjust target_size according to your model's input shape\n",
    "img_array = image.img_to_array(img)\n",
    "img_array = np.expand_dims(img_array, axis=0)\n",
    "img_array = preprocess_input(img_array)\n",
    "\n",
    "# Step 3: Make predictions\n",
    "predictions = loaded_model.predict(img_array)\n",
    "\n",
    "# Display the predictions\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb922e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "\n",
    "# Assuming maml_model is the variable holding your trained model\n",
    "# maml_model = create_maml_model(base_model, num_classes)  # Replace with your model creation code\n",
    "\n",
    "# Path to the folder containing images\n",
    "folder_path = 'C:\\\\Users\\\\oscar\\\\Documents\\\\MOMOCS\\\\select2'\n",
    "\n",
    "# List all files in the folder\n",
    "image_files = os.listdir(folder_path)\n",
    "\n",
    "# Iterate over each image file\n",
    "for file_name in image_files:\n",
    "    # Load the input image\n",
    "    img_path = os.path.join(folder_path, file_name)\n",
    "    img = image.load_img(img_path, target_size=(250, 200))\n",
    "    img_array = image.img_to_array(img)\n",
    "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
    "\n",
    "    # Preprocess the input image using ResNet50 preprocessing\n",
    "    img_array = preprocess_input(img_array)\n",
    "\n",
    "    # Use the model to make predictions\n",
    "    predictions = maml_model.predict(img_array)\n",
    "\n",
    "    # Get the predicted class index\n",
    "    predicted_class_index = np.argmax(predictions[0])\n",
    "\n",
    "    # Get the predicted class probability\n",
    "    predicted_class_probability = predictions[0][predicted_class_index]\n",
    "\n",
    "    # Get class labels (assuming you have them)\n",
    "    class_labels = ['croc', 'hyena', \"leopard\",\"lion\"]  # Example class labels\n",
    "\n",
    "    # Print the result\n",
    "    print(f'Image: {file_name}')\n",
    "    print(f'Predicted class: {class_labels[predicted_class_index]}')\n",
    "    print(f'Predicted class probability: {predicted_class_probability:.4f}')\n",
    "\n",
    "    # Print probabilities for other classes\n",
    "    print('Probabilities for other classes:')\n",
    "    for i, prob in enumerate(predictions[0]):\n",
    "        print(f'{class_labels[i]}: {prob:.4f}')\n",
    "    print('---------------------------------------------')"
   ]
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   "source": []
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